A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features

Abstract Aims The early stages of Alzheimer's disease (AD) are no longer insurmountable. Therefore, identifying at‐risk individuals is of great importance for precise treatment. We developed a model to predict cognitive deterioration in patients with mild cognitive impairment (MCI). Methods Based on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, we constructed models in a derivation cohort of 761 participants with MCI (138 of whom developed dementia at the 36th month) and verified them in a validation cohort of 353 cognitively normal controls (54 developed MCI and 19 developed dementia at the 36th month). In addition, 1303 participants with available AD cerebrospinal fluid core biomarkers were selected to clarify the ability of the model to predict AD core features. We assessed 32 parameters as candidate predictors, including clinical information, blood biomarkers, and structural imaging features, and used multivariable logistic regression analysis to develop our prediction model. Results Six independent variables of MCI deterioration were identified: apolipoprotein E ε4 allele status, lower Mini‐Mental State Examination scores, higher levels of plasma pTau181, smaller volumes of the left hippocampus and right amygdala, and a thinner right inferior temporal cortex. We established an easy‐to‐use risk heat map and risk score based on these risk factors. The area under the curve (AUC) for both internal and external validations was close to 0.850. Furthermore, the AUC was above 0.800 in identifying participants with high brain amyloid‐β loads. Calibration plots demonstrated good agreement between the predicted probability and actual observations in the internal and external validations. Conclusion We developed and validated an accurate prediction model for dementia conversion in patients with MCI. Simultaneously, the model predicts AD‐specific pathological changes. We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.


| INTRODUC TI ON
Alzheimer's disease (AD) is a progressive neurodegenerative disease characterized by cognitive decline that affects millions of people worldwide and places an enormous burden on the public. 1The view that AD is irreversible is gradually being challenged.Recently, a series of encouraging clinical trial results have suggested the effectiveness of monoclonal antibodies, such as lecanemab and donanemab, in preventing disease progression in the early stages of AD. 2,3 Mild cognitive impairment (MCI) is the earliest recognizable stage of objective cognitive impairment in clinical practice and serves as an early warning of future dementia. 4However, most MCI cases are stable, and the annual conversion rate to dementia may be less than 10%. 4,5Therefore, identifying the risk factors for cognitive decline and establishing a predictive model has substantial practical implications, especially since they would offer targets for interventions to delay or even halt disease progression.
In previous studies, we and other researchers used various complex methods to identify the early stages of AD or predict cognitive deterioration, such as extracting high-dimensional features from multiparametric magnetic resonance imaging (MRI), 6,7 exploring brain glucose metabolic patterns using positron emission tomography (PET), 8,9 or analyzing amyloidβ (Aβ) levels in neuronal-derived extracellular vesicles. 10Although these models exhibit excellent discriminative abilities, their practical applications are limited.The pathological core features of AD, including the extracellular deposition of Aβ and the intraneuronal presence of aggregated hyperphosphorylated tau proteins, 11 as well as various non-specific features of neuroinflammation and synaptic dysfunction, can all be quantified using cerebrospinal fluid (CSF)   or PET analysis, and their levels are significantly correlated with cognitive ability. 12However, these methods are expensive and invasive, making them more suitable for confirmation than prediction.Alternatively, blood biomarkers and structural MRI are more clinically feasible.
In recent years, blood biomarkers have become increasingly popular with the development of platforms for hypersensitive detection. 13,14For example, increased neurofilament light (NFL) levels in blood appear to reflect the severity of AD-related neurodegeneration, 15 and blood phosphorylated tau contributes to the recognition of AD-specific pathologies across the cognitive continuum. 16These biomarkers have the potential to serve as valuable prognostic and susceptibility markers.The anatomical features on MRI are classic markers of neurodegeneration.In addition, a close connection between AD and cerebrovascular injuries has been emphasized. 17Similar to macroscopic infarcts, cerebral microangiopathy contributes substantially to dementia. 18Therefore, imaging features of cerebral small vessel disease may also indicate future cognitive decline. 19e combination of blood and MRI biomarkers holds significant potential for improving the accuracy of predicting dementia conversion, given their complementary nature in capturing different aspects of the disease.In this study, based on a sample of well-characterized participants, we aimed to: (1) propose a novel, practical, and convenient multivariable model for predicting cognitive deterioration based on basic clinical, blood, and imaging features while further establishing a quantitative risk scoring system; and (2) evaluate the model's ability to estimate AD core features.
1][22] We hope to provide an accurate and convenient diagnostic tool that will ultimately lead to more appropriate referrals and interventions for patients with early-stage AD.They had available plasma NFL, tau phosphorylated at residue 181 (pTau181), and structural MRI data and underwent clinical follow-up after 36 months.By contrast, the participants in the validation cohort were cognitively healthy at baseline.In addition, we screened participants with available AD CSF core biomarkers to form the "CSF validation cohorts" to clarify the model's ability to predict AD core features (A/T profile), which are in accordance with the latest research framework. 13,23All enrolled participants

| CSF biomarkers
Alzheimer's disease core biomarkers, including CSF Aβ 42 , pTau181, and total tau levels, were measured using fully automated Roche Elecsys® immunoassays as described previously. 25Participants in the CSF validation cohorts were classified as having high brain Aβ loads (A+) or fibrillar tau (T+) according to predefined principles that utilized established cutoff values of <977 pg/mL for CSF Aβ 42 and >27 pg/mL for phosphorylated tau. 26Participants were considered to have core AD features (AD+) if they were both A+ and T+.Details regarding CSF collection and detection are available at http:// adni.loni.usc.edu/ .

| Plasma biomarkers
The measurement procedures for plasma pTau181 and NFL levels have been comprehensively described in previous publications. 27,28Briefly, the assay was based on a single-molecule array method using a combination of commercial monoclonal antibodies.The concentrations used in this study were all above the lower limit of detection.

| Neuroimaging acquisition and processing
Structural imaging was performed using a 1.5-or 3.0-T MRI scanner with a 3D T1-weighted MPRAGE sequence.A region-of-interest analysis using FreeSurfer (version 4.3 for 1.5-T data and version 5.1 for 3.0-T data; http:// surfer.nmr.mgh.harva rd.edu/ ) was performed to obtain regional morphological parameters, including volume and cortical thickness.The results were included only if they passed quality control.0][31][32] Based on this, we considered the volume of the hippocampus and amygdala and the thickness of the entorhinal cortex, fusiform gyrus, inferior parietal lobule, inferior temporal gyrus, middle temporal gyrus, and parahippocampal gyrus as potential predictor variables.
F I G U R E 1 Formation of the cohorts.We aimed to establish a clinical model for predicting future cognitive deterioration based on clinical information, blood biomarkers, and sMRI indicators.By September 2023, 761 participants were selected, called Cohort 1.This cohort was used to screen candidate predictor variables and build prediction models (derivation cohort).Cohort 2 included 353 NCs for model validation (validation cohort).Furthermore, participants without CSF data were excluded to form the CSF validation cohorts, including a subset of Cohort 1, a subset of Cohort 2, and a re-screened cohort of 1303 participants (Cohort 3).They were classified as having high brain Aβ loads (A+) or fibrillar tau (T+) according to a priori principles; AD+ means both A+ and T+.AD, Alzheimer's disease; ADNI, Alzheimer's Disease Neuroimaging Initiative; Aβ, amyloidβ; CSF, cerebrospinal fluid; m, month; MCI, mild cognitive impairment; NCs, cognitively normal controls; NFL, neurofilament light; pTau181, phosphorylated-tau181; sMRI, structural magnetic resonance imaging.
White matter hyperintensities (WMH) and silent brain infarcts are the classical imaging hallmarks of cerebral small vessel disease. 33These were considered candidate predictor variables in the current study.The measurements were performed in the DeCarli Lab (UC-Davis).For WMH, a Bayesian approach was used to segment the high-resolution 3D T1 and fluid-attenuated inversion recovery (FLAIR) sequences. 34Briefly, images were processed to (1) exclude non-brain tissues, (2) spatially align, and (3) remove MRI field artifacts.The images were then warped to a standard template space where the prior probability of WMH occurrence and the FLAIR signal characteristics of the WMH were modeled at every location in the cerebral white matter.This prior information, together with the signal intensities of the FLAIR images in question, was used to identify WMH.With the help of an image analysis system, 35 which allows simultaneous projection of the complete imaging sequence dataset at three times magnified, infarcts were identified by experienced neurologists using T1weighted and FLAIR MRI images (≥3 mm).According to previous classification methods, [36][37][38] the participants were grouped based on the presence or absence of infarcts, the number of infarcts (none, single, or multiple), and the presence or absence of infarcts at a specific anatomical site, such as the thalamus, cortex, or infratentorial structure.

| Candidate predictors and study outcome
We aimed to establish a convenient model for predicting future cognitive deterioration with practical implications.0][31][32] Before model development, all predictor variables were evaluated for correlations to avoid multicollinearity in the multivariable logistic analysis.
The outcome of the study was the deterioration in cognitive function after 36 months.Currently, AD is recognized as a distinct entity and is defined pathologically by the presence of specific A/T profiles 13,23 ; thus, we also analyzed the model's ability to predict the core features of AD.

| Statistical analysis
Basic participant characteristics were summarized as numbers (%) or means (standard deviations) for categorical and continuous variables, respectively.Groups were compared using chi-square tests for categorical variables and independent-sample t-tests for continuous variables.
The sample size for this study was based on feasibility considerations, and a post hoc sample calculation was performed.According to the guidelines for fitting multivariable models, we used the following formula: N = (n × 15)/I, where N represents the sample size, n represents the number of variables in the final multivariable logistic model (n = 6), and I represents the incidence of conversion from MCI to dementia within 36 months.Considering the reported conversion rates of 6.0% to 44.8% in the literature 4,5 and the 18.1% rate observed in our data, we set the rate at 20.0%.Thus, the test results demonstrate that the sample size in our study was sufficient to develop a prediction model.

| Prediction model development
We evaluated 32 independent candidate variables for their potential to predict future cognitive deterioration.We performed univariable and multivariable logistic regression analyses to assess the effects of these candidate predictors.Variables associated with MCI deterioration (p < 0.1) in the univariable analysis were included in the multivariable regression analysis.Two independent and parallel methods were used to develop a parsimonious predictor model while minimizing overfitting: stepwise backward variable elimination and least absolute shrinkage and selection operator (Lasso) regression, with a significance level of 0.05 for variable retention.The overall goodness-of-fit of the models was assessed using the Akaike Information Criteria Moreover, we integrated variables predictive of dementia conversion into a clinical score using the multivariable logistic regression model.
We assigned a risk point value for each variable proportional to the respective β coefficients.All β coefficients were standardized so that the lowest one had a point value of 0.5 to make the risk scores close to an integer and facilitate intuitive use.

| Prediction model validation
We internally validated the prediction model in the derivation cohort using the bootstrap method with 1000 repetitions and further externally validated the prediction model in the validation and CSF validation cohorts.All statistical analyses were performed using the R programming language (version 4.2.1) and GraphPad Prism (version 9.4.0).

| Participant characteristics
A total of 761 participants with MCI were included in the derivation cohort (Cohort 1; Figure 1).Of them, 432 (56.8%) were men, and the average age was 72.9 years (range: 55.2-92.6 years).At the 36th month, 138 (18.1%) participants developed dementia.As a subset, 575 individuals (75.6%) were evaluated for cerebral WMH and infarcts, and there were no differences between the two groups (Table S1).Three hundred and fifty-three NCs, with a similar proportion of men (49.0%), were included in the validation cohort (Cohort 2; Figure 1).The average age was 76.3 years (range: 56.1-95.3years).At the 36th month of follow-up, 73 participants (20.7%) had cognitive deterioration, of whom 54 developed MCI and 19 developed dementia.Detailed demographic and clinical characteristics are presented in Table 1.
Patients with available AD CSF core biomarkers were selected as the CSF validation cohorts (Figure 1), which included subsets of Cohorts 1 (n = 378), 2 (n = 84), and 3 (n = 1303).Cohort 3 contained these two subsets and consisted of 457 NCs, 635 patients with MCI, and 211 patients with dementia.Table S2

| Identifying independent risk factors for conversion from MCI to dementia
In the derivation cohort, univariable analysis identified a significant correlation between MCI deterioration and 22 risk factors consistent Volume of the hippocampus and amygdala was presented as the ratio of the regional volume to TIV, multiplied by a factor of 100.

TA B L E 1
Baseline characteristics of the derivation and validation cohorts.
with previous reports (Table S3): older age, lower years of education, APOE ε4 allele status, lower scores on the MMSE scale, higher levels of plasma NFL and pTau181, and all selected MRI indicators (bilateral).
Each significant variable was included in a multivariable logistic regression model using a backward elimination procedure or a Lasso regression (Figure S1).After screening, the Lasso method resulted in one fewer variable, the thickness of the right inferior temporal cortex, compared with observations in the previous method.Age was excluded by both methods.Finally, six independent variables associated with MCI deterioration were identified by multivariable logistic regression analysis: APOE ε4 allele status, lower MMSE scale scores, higher plasma pTau181 levels, smaller volume of the left hippocampus and right amygdala, and reduced thickness of the right inferior temporal cortex (Table 3).

| Developing a prediction model
The equation for the prediction model for conversion from MCI to dementia is as follows: where: We developed a risk score system based on multivariable logistic regression.We assigned point values to each parameter based on the β coefficient: an APOE ε4 allele count of zero scored 0 points, one scored 2 points, and two scored 4 points; an MMSE score ≥28 scored 0 points, a score between 24 and 27 scored 2.5 points, and a score ≤23 scored 5.5 points; an intracranial volume ratio of the left hippocampus ≥0.250% scored 0 points, between 0.201% and 0.249% scored 1 point, and ≤0.200% scored 2 points; an intracranial volume ratio of the right amygdala ≥0.100% scored 0 points, between 0.081% and 0.099% scored 1 point, and ≤0.080% scored 2 points; cortical thickness of the right inferior temporal cortex (mm) ≥2.800 scored 0 points, between 2.601 and 2.799 scored 1 point, and ≤2.600 scored 2.5 points; plasma pTau181 levels (pg/ mL) ≤12,000 scored 0 points, between 12,001 and 19,999 scored 0.5 points, and ≥20,000 scored 1 point (Table 4).Figure S2 and Table S4.The lowest score (0 points) and the highest score (17.0 points) indicated a 1.5% and 93.3% risk of MCI deterioration, respectively.Moreover, the risk heatmap and nomogram of the model are provided for convenient use in clinical practice (Figure 2; Figure S3).

| Evaluating the performance of the model
To evaluate the performance of the model, we first conducted an internal validation using bootstrap resampling with 1000 repetitions (Figure 3A,D).The model exhibited strong discrimination in predicting conversion from MCI to dementia, with an AUC of 0.848 (95% CI 0.815-0.882).The calibration plots demonstrated excellent agreement between the predicted probability and the actual observations.
We further validated the model in an independent cohort (Cohort 2).
The AUC of the model for predicting conversion from NCs to MCI or dementia was 0.681 (95% CI 0.605-0.751),demonstrating moderate discrimination capability (Figure 3B).The calibration curves also deviated from the diagonal and did not achieve statistical significance (p < 0.05; Figure 3E).However, if we focus only on dementia conversion, the performance is excellent, with an AUC of 0.844 (95% CI 0.723-0.936;Figure 3C).Figure 3F shows the calibration curve, which is statistically significant (p > 0.05), indicating good consistency between the predicted and actual observations.Finally, we assessed the net benefit of the intervention strategy using the risk prediction model compared to a treat-all or treat-none strategy.The DCA indicated a positive net benefit in the internal validation, starting from a 10% probability threshold (Figure 3G).However, the analysis suggested that the benefit window in the external validation was relatively small (Figure 3H,I), likely due to the different baseline diagnoses.Volume of the hippocampus and amygdala was presented as the ratio of the regional volume to TIV, multiplied by a factor of 100.

TA B L E 3
Final multivariable logistic regression model of dementia conversion for patients with MCI in the derivation cohort.

TA B L E 4
The model score for prediction of MCI deterioration.4G) and 3 (Figure 4I), but a narrow benefit window in the subset of Cohort 2 (Figure 4H).Furthermore, participants in Cohort 3 were divided according to diagnosis, and the results

Risk factor Points
showed that the model's discriminatory capability was acceptable (Table S5), particularly in the MCI subset (A+ vs.

| DISCUSS ION
Generally, AD is irreversible once it reaches the dementia stage because the degree of neuronal loss becomes irreparable. 11By contrast, the early stages may be partially reversible with timely and appropriate treatment.1000 iterations suggested that the model could reliably predict dementia conversion in patients with MCI at the 36-month mark.
External validation suggested that the model could also predict future cognitive deterioration in NCs and identify participants with AD-specific pathologies.
Unexpectedly, both WMH and silent infarcts were eliminated.
[41] Cross-sectional studies have suggested that WMH are associated with cognitive function across all major domains. 42According to several postmortem investigations, individuals with dementia and pure AD pathologies are uncommon; the majority of patients frequently exhibit mixed alterations involving both AD and vascular pathologies. 43,44Each thrombotic event promotes Aβ production, 45 and the prevailing view is that cerebrovascular lesions lower the threshold for AD symptoms. 17,46However, it should be noted that participants in the ADNI dataset typically have a low vascular risk burden, resulting in small brain lesions.Additionally, we focused on the total volume of WMH rather than their patterns (punctuated or confluent) or locations (deep or periventricular), and cognitive decline may only be associated with specific patterns and locations of WMH.Theoretically, blood NFL reflects the severity of atrophy, hypometabolism, and the decline in white matter integrity, particularly in regions typically affected by AD. 15 Its levels begin to rise 6.8 to 15 years before the expected symptom onset in patients with familial AD. 49,50 However, this was not included in our prediction model.
This omission is due to NFL being primarily a marker of neurodegeneration, possibly with less weight compared with that of MRI structural abnormalities.Age was likely excluded for similar reasons.The final model included the volumes of the hippocampus and amygdala and the thickness of the inferior temporal cortex.These results align with observations from clinical practice and several prior studies demonstrating early and profound atrophy in these regions among patients with MCI and dementia. 31,51To avoid missing information, we included both left and right indicators rather than their summations.Previous studies on AD diagnosis and prediction also indicated a biased advantage. 6,9,30,52The specific reason for this remains unclear and may be related to brain laterality.as those based on deep learning combined with multimodal and multidimensional data, achieved better performance, with accuracy rates exceeding 90%.However, for clinicians, these methods are obscure, and the features obtained are difficult to interpret.
Moreover, deep learning is an end-to-end black box that lacks interpretable features. 53On the other hand, modeling using overly simple features decreases classification accuracy.This study has several limitations.First, according to the ADNI inclusion/exclusion criteria, the participants were mostly white, highly educated, and had relatively low vascular risk burdens.In the future, it will be necessary to verify the prediction model using other datasets.Second, this study was a retrospective analysis and thus bears the inherent limitations of such studies.Further prospective studies with larger cohorts are warranted.Third, due to the limitations of the dataset, we did not consider blood Aβ, an indicator of intracranial Aβ deposition, 13,14 or blood glial fibrillary acidic protein, an indicator of astrocytic activation, 13 as candidate predictor variables.The

| CON CLUS IONS
At the 2023 Alzheimer's Association International Conference, the value of blood markers was highlighted to an unprecedented extent and will be further popularized through market promotion. 13In this context, we aimed to establish a model for predicting cognitive deterioration using typical blood markers and conventional structural MRI.Our model includes predictors that are routinely available and easily accessible for incorporation into computer systems within healthcare services, or for integration into an application.We provided an easy-to-use and convenient nomogram and risk heatmap that can be easily adopted in clinical practice, in addition to a risk score.In summary, we developed and validated a highly discriminative, well-calibrated, and parsimonious prediction

CO N FLI C T O F I NTER E S T S TATEM ENT
On behalf of all authors, the corresponding author confirms no conflict of interest.All authors agreed to the publication of the manuscript in its current form.

2. 1 |
Participants and study designData used in the preparation of this article were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu).The ADNI was launched in 2003 as a publicprivate partnership, led by Principal Investigator Michael W.Weiner, MD.The primary goal of ADNI has been to test whether serial MRI, PET, other biological markers, and clinical and neuropsychological assessment can be combined to measure the progression of MCI and early AD.For up-to-date information, see http:// www.adni-info.org.

Figure 1
Figure 1 illustrates the screening process of the participants.Briefly, we used Cohort 1 as a derivation cohort to select candidate predictor variables and construct prediction models and Cohort 2 as an independent validation cohort.The participants in the derivation cohort were diagnosed with MCI at baseline.

| 3 of 13 LI
et al. provided detailed clinical information, including demographic data, apolipoprotein E (APOE) status, and neuropsychological test results.The tests included the Clinical Dementia Rating (CDR) scale, Mini-Mental State Examination (MMSE), Logical Memory test, etc.The ADNI database classified individuals clinically as cognitively normal controls (NCs; MMSE score: ≥24, CDR score: 0), those with MCI (MMSE score: ≥24, CDR score: 0.5, and objec-tive memory loss measured using education-adjusted scores on delayed recall of logical memory), or those with AD dementia following predefined criteria.24

(
AIC) and likelihood ratio tests.Model discrimination was compared based on C-statistics, integrated discrimination improvement (IDI), and net reclassification index (NRI).A risk heatmap and a nomogram were established based on multivariable logistic regression analysis.
We assessed discrimination, calibration, and net benefit to evaluate the performance of the model.Discrimination was calculated using concordance statistics (area under the receiver operating characteristic curve [AUC]).Calibration was assessed using the Hosmer-Lemeshow test and visualized with the calibration plot.A perfect plot is indicated by the 45° diagonal line.To further investigate the model's clinical utility, decision curve analysis (DCA) was performed to determine the net clinical benefit.

2 , 3
Identifying patients with unstable MCI is crucial for precise treatment and, ultimately, for reducing the social burden.In the present study, we established a prediction model based on clinical information, blood biomarkers, and structural MRI indicators.Internal validation using a bootstrap of F I G U R E 2 The risk estimation of dementia conversion in patients with MCI.Chart based on six included parameters: APOE ε4 status, MMSE score, intracranial volume ratio of left hippocampus (%), intracranial volume ratio of right amygdala (%), cortical thickness of right inferior temporal cortex, and plasma pTau181 levels.APOE, apolipoprotein E; MCI, mild cognitive impairment; MMSE, mini-mental state examination; pTau181, phosphorylated-tau181.

F I G U R E 3
The predictive accuracy, calibration plot and decision curve analysis of the model for predicting clinical deterioration.The predictive accuracy of the model for predicting clinical deterioration (A-C).(A) ROC curve of the clinical model for predicting dementia conversion for patients with MCI in the derivation cohort (cohort 1); the AUC is 0.848 (95% CI 0.815-0.882).(B) ROC curve of the model for predicting cognitive deterioration (MCI or dementia) for NCs in the validation cohort (cohort 2); the AUC is 0.681 (95% CI 0.605-0.751).(C) ROC curve of the model for predicting dementia conversion for NCs in the validation cohort (cohort 2); the AUC is 0.844 (95% CI 0.723-0.936).Calibration plot of the clinical model for predicting cognitive deterioration (D-F).(D) model in the derivation cohort (cohort 1).(E) Model in the validation cohort (cohort 2; MCI or dementia conversion for NCs).(F) Model in the validation cohort (cohort 2; dementia conversion for NCs).The model-predicted probability of cognitive deterioration was plotted on the x-axis; actual probability was plotted on the y-axis.An ideal calibration plot is indicated by a 45° diagonal line; Hosmer-lemeshow test value (p) was listed.Decision curve analysis of the clinical model for predicting clinical deterioration in (G), the derivation cohort (cohort 1), (H) validation cohort (cohort 2; MCI or dementia conversion for NCs), and (I) validation cohort (cohort 2; dementia conversion for NCs).Black line: assumes no patient has clinical deterioration.Gray dashed line: assumes all patients have clinical deterioration.These two lines serve as a reference.AUC, area under the curve; CI, confidence interval; MCI, mild cognitive impairment; NC, cognitively normal control; ROC, receiver operating characteristic.In the multivariate analysis, we used stepwise backward variable elimination and Lasso regression to address multicollinearity and simplify variables.We then compared models in terms of fit and discrimination ability.The resulting model integrates genetic susceptibility, objective cognitive evaluation, structural indicators, and plasma biomarkers of pTau181, which complement one another, allowing the model to balance simplification with differentiation.

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Several prediction models have been developed for dementia conversion in patients with MCI.Zhao et al. added hippocampal radiomic features related to changes in MMSE scores sequentially into a logistic regression, improving the AUC of the basic prediction model from 0.65 to 0.82.7 A recently published study systematically reviewed the application of machine learning in predicting dementia conversion20 and found that the average accuracy was approximately 75%, while more complex models, such F I G U R E 4 The predictive accuracy, calibration plot and decision curve analysis of the model for predicting AD CSF core features.The predictive accuracy of the model for predicting AD CSF core features (A+, T+, and AD+) in CSF validation cohorts (A-C).(A) Subset of cohort 1, (B) subset of cohort 2, (c) Cohort 3. Calibration plot of the clinical model for predicting AD+ subjects (D-F).(D) model in the subset of cohort 1, (E) model in the subset of cohort 2, (F) model in the cohort 3. The model-predicted probability of cognitive deterioration was plotted on the x-axis; actual probability was plotted on the y-axis.An ideal calibration plot is indicated by a 45° diagonal line; Hosmerlemeshow test value (p) was listed.Decision curve analysis of the clinical model for predicting AD+ subjects in (G), subset of cohort 1, (H) subset of cohort 2, and (I) cohort 3. Black line: assumes no patient has clinical deterioration.Gray dashed line: assumes all patients have clinical deterioration.These two lines serve as a reference.Participants were classified as having high brain Aβ loads (A+) or fibrillar tau (T+) according to a priori principles; AD+ means both A+ and T+.AD, Alzheimer's disease; AUC, area under the curve; CI, confidence interval; CSF, cerebrospinal fluid.et al.
inclusion of these markers may further improve the model.Fourth, the sample size of the NCs may have been insufficient because of the low dementia conversion rate.Validation requires a large sample of NCs.Fifth, although the model's ability to predict AD CSF core features is already prominent, this value is likely underestimated due to changes in outcome events.It is difficult for a single model to balance these two outcomes.
model to predict dementia conversion risk in participants without dementia.The model also has the ability to predict AD pathological core features, allowing for early diagnosis and intervention, ultimately contributing to more precise clinical care and better healthcare resource allocation.by the Laboratory for Neuro Imaging at the University of Southern California.The authors wish to acknowledge the group of Professor Shao-Wen Tang (Department of Epidemiology, School of Public Health, Nanjing Medical University) for their assistance in statistical methods for this study.FU N D I N G I N FO R M ATI O N This work was supported by grants "82300149" from the National Natural Science Foundation of China, grants "2023M741462" from the China Postdoctoral Science Foundation, grants "MXJL202211" from the First Affiliated Hospital of Nanjing Medical University, grants "PY2023018" from the Young Scholars Fostering Fund of the First Affiliated Hospital of Nanjing Medical University, and grants "2023ZB308" and "2023ZB182" from the Department of Human Resources and Social Security of Jiangsu Province.
We hope that this model will contribute to more precise clinical treatment and better healthcare resource allocation.